This project seeks to develop new statistical tools for evaluating gene-environment interactions and genetic susceptibility. Work has proceeded in the area of improving study designs. Geneticists have proposed using cases and their parents (case-parents triads) to study genetic effects on disease risk while overcoming the problem of population stratification or admixture. If a population consists of a number of distinct subpopulations that differ in baseline disease rates and in the frequency of a genetic variant, case-control studies may find associations between the variant and disease that arise simply because of the differing characteristics of the subpopulations and, consequently, lack etiologic import. Case-parents triad designs can eliminate such potentially misleading associations. We have investigated ways to analyze case-parents data for studying gene-environment interaction and found that a commonly used approach is invalid. We have proposed an approach based on loglinear models that provides valid inference when the usual approach fails. We are also investigating designs that augment case-parents triads with control-parent triads to overcome some shortcomings of using case-parents triads alone. Such designs should more fully extend protection against population structure to studies of genotype and exposure together.